Open Access

ARTICLE

Priority-Aware Resource Allocation for VNF Deployment in Service Function Chains Based on Graph Reinforcement Learning

Seyha Ros^1,#, Seungwoo Kang^1,#, Taikuong Iv¹, Inseok Song¹, Prohim Tam², Seokhoon Kim^1,3,*

1 Department of Software Convergence, Soonchunhyang University, Asan, 31538, Republic of Korea
2 School of Digital Technologies, American University of Phnom Penh, Phnom Penh, 12106, Cambodia
3 Department of Computer Software Engineering, Soonchunhyang University, Asan, 31538, Republic of Korea

* Corresponding Author: Seokhoon Kim. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Graph Neural Networks: Methods and Applications in Graph-related Problems)

Computers, Materials & Continua 2025, 83(2), 1649-1665. https://doi.org/10.32604/cmc.2025.062716

Received 25 December 2024; Accepted 06 March 2025; Issue published 16 April 2025

Abstract

Recently, Network Functions Virtualization (NFV) has become a critical resource for optimizing capability utilization in the 5G/B5G era. NFV decomposes the network resource paradigm, demonstrating the efficient utilization of Network Functions (NFs) to enable configurable service priorities and resource demands. Telecommunications Service Providers (TSPs) face challenges in network utilization, as the vast amounts of data generated by the Internet of Things (IoT) overwhelm existing infrastructures. IoT applications, which generate massive volumes of diverse data and require real-time communication, contribute to bottlenecks and congestion. In this context, Multi-access Edge Computing (MEC) is employed to support resource and priority-aware IoT applications by implementing Virtual Network Function (VNF) sequences within Service Function Chaining (SFC). This paper proposes the use of Deep Reinforcement Learning (DRL) combined with Graph Neural Networks (GNN) to enhance network processing, performance, and resource pooling capabilities. GNN facilitates feature extraction through Message-Passing Neural Network (MPNN) mechanisms. Together with DRL, Deep Q-Networks (DQN) are utilized to dynamically allocate resources based on IoT network priorities and demands. Our focus is on minimizing delay times for VNF instance execution, ensuring effective resource placement, and allocation in SFC deployments, offering flexibility to adapt to real-time changes in priority and workload. Simulation results demonstrate that our proposed scheme outperforms reference models in terms of reward, delay, delivery, service drop ratios, and average completion ratios, proving its potential for IoT applications.

---

Keywords

---